Java Fast Sockets: Enabling high-speed Java communications on high performance clusters

Taboada, Guillermo L.; Touriño, Juan; Doallo, Ramón

doi:10.1016/j.comcom.2008.08.012

Cited by 19 publications

(15 citation statements)

References 19 publications

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“…This communication device accesses HPC hardware through JNI using either standard JVM IO sockets (TCP) or Java Fast Sockets (JFS) [16], a high-performance Java IO sockets (TCP) implementation, as it can be seen in Fig. 1.…”

Section: Overview Of the F-mpj Communication Supportmentioning

confidence: 99%

“…However, in order to avoid these drawbacks, F-MPJ has integrated in iodev the high-performance Java sockets library JFS (Java Fast Sockets) [16], in a portable and efficient way. Thus, JFS boosts F-MPJ communication efficiency by: (1) avoiding primitive data type array serialization through an extended API that allows direct communication of primitive data type arrays (see Listing 3); (2) making unnecessary the data buffering when sending/receiving portions of primitive data type arrays using offset and length parameters (see JFS API in Listing 3 and its application in Listing 4); (3) avoiding the copies between the JVM data and native memory thanks to JFS's zero-copy protocol; and (4) providing efficient support on shared memory, and Gigabit Ethernet, SCI, Myrinet and InfiniBand networks through the use of the underlying libraries specified in Fig.…”

Section: Java Fast Sockets Support In Iodevmentioning

confidence: 99%

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F-MPJ: scalable Java message-passing communications on parallel systems

2009

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This paper presents F-MPJ (Fast MPJ), a scalable and efficient MessagePassing in Java (MPJ) communication middleware for parallel computing. The increasing interest in Java as the programming language of the multi-core era demands scalable performance on hybrid architectures (with both shared and distributed memory spaces). However, current Java communication middleware lacks efficient communication support. F-MPJ boosts this situation by: (1) providing efficient non-blocking communication, which allows communication overlapping and thus scalable performance; (2) taking advantage of shared memory systems and highperformance networks through the use of our high-performance Java sockets implementation (named JFS, Java Fast Sockets); (3) avoiding the use of communication buffers; and (4) optimizing MPJ collective primitives. Thus, F-MPJ significantly improves the scalability of current MPJ implementations. A performance evaluation on an InfiniBand multi-core cluster has shown that F-MPJ communication primitives outperform representative MPJ libraries up to 60 times. Furthermore, the use of F-MPJ in communication-intensive MPJ codes has increased their performance up to seven times.

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Section: Overview Of the F-mpj Communication Supportmentioning

confidence: 99%

Section: Java Fast Sockets Support In Iodevmentioning

confidence: 99%

F-MPJ: scalable Java message-passing communications on parallel systems

2009

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“…Thus, sockets have been the choice for implementing in Java the lowest level of network communication. However, Java sockets usually lack efficient highspeed networks support [29], so it has to resort to inefficient TCP/IP emulations for full networking support. Examples of TCP/IP emulations are IP over InfiniBand (IPoIB), IPoMX on top of the Myrinet low-level library MX (Myrinet eXpress), and SCIP on SCI.…”

Section: Java Socketsmentioning

confidence: 99%

Java for high performance computing

Taboada

Touriño

Doallo

2009

Proceedings of the 7th International Conference on Principles and Practice of Programming in Java

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The rising interest in Java for High Performance Computing (HPC) is based on the appealing features of this language for programming multi-core cluster architectures, particularly the built-in networking and multithreading support, and the continuous increase in Java Virtual Machine (JVM) performance. However, its adoption in this area is being delayed by the lack of analysis of the existing programming options in Java for HPC and evaluations of their performance, as well as the unawareness of the current research projects in this field, whose solutions are needed in order to boost the embracement of Java in HPC.This paper analyzes the current state of Java for HPC, both for shared and distributed memory programming, pre--sents related research projects, and finally, evaluates the performance of current Java HPC solutions and research developments on a multi-core cluster with a high-speed network, InfiniBand, and a 24-core shared memory machine. The main conclusions are that: (1) the significant interest on Java for HPC has led to the development of numerous projects, although usually quite modest, which may have prevented a higher development of Java in this field; and (2) Java can achieve almost similar performance to native languages, both for sequential and parallel applications, being an alternative for HPC programming. Thus, the good prospects of Java in this area are attracting the attention of both industry and academia, which can take significant advantage of Java adoption in HPC.

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“…Our assumption is a true proposition, as its proof can be inferred from the section of experimental results, where both experimental and theoretical results indicate the same behavior of the system. However, there is also third party socket library which claims to be faster and much more efficient than the Java native sockets [14]. Another approach is also Java Remote Method Invocation (RMI) which is also based on native Java sockets allowing remote methods calls.…”

Section: Our Implementationmentioning

confidence: 99%

“…The current implementation of this study could be improved in terms of elapsed time during the training by using third party libraries available for Java socket programming such as Java Fast Sockets (JFS) [14]. Furthermore, object streaming method for communication which involves serialization can be improved by replacing serialization with other approaches e.g., GSON and XML data formats.…”

Section: Future Workmentioning

confidence: 99%

Parallel computing for artificial neural network training

Gursoy¹,

Sharif²

2018

PEN

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As an enormous computing power is required to get knowledge from a large volume of data, the parallel and distributed computing is highly recommended to process them. Artificial Neural Networks (ANNs) need as much as possible data to have high accuracy, whereas parallel processing can help us to save time in ANNs training. In this paper, exemplary parallelization of artificial neural network training by dint of Java and its native socket libraries has been implemented. During the experiments, it has been noticed that Java native socket implementation tends to have memory issues when a large amount of training datasets are involved in training. It has been remarked that exemplary parallelization of artificial neural network training cannot outperform drastically when additional nodes are introduced into the system after a certain point. This is comprehensively due to the network communication complexity in the system.Corresponding Author: Name Md. Haidar Sharif Affiliation International Balkan University Address Skopje, Republic of Macedonia Phone +38923214831 Email msharif@ibu.edu.mk IntroductionParallel and distributed computing are required to get knowledge from a large volume of data. Artificial Neural Networks (ANNs) need to have high accuracy, whereas parallel processing can help us to save time in ANNs training. In the literature, many articles on parallelization of neural network pieces of training can be found. Some examples are given herewith. A parallelized Back-Propagation Neural Network (BPNN) based on MapReduce computing model was introduced by Liu et al. [1]. A cascading model based classification approach was implemented to introduce fault tolerance, data replication, and load balancing. Nodes were trained for a specific class available in training dataset. As a result, a much stronger classifier could be built by the weak classifiers in the cluster. A parallelizing technique for the training of neural networks on the cluster computer was presented by Dahl et al. [2]. A full neural network was copied at each cluster node for the eight-bit parity problem. The subset of the training data in each node was randomly selected at each epoch, which is the date and time related to a computer's clock [3]. An improvement up to a factor of 11 in terms of training time as compared to sequential training was demonstrated through the obtained results. The type of neural network problem was mentioned. A different kind of exemplary parallelization was implemented. The communication costs of the approach were analysed. But it was not recommended the node parallelism in cluster computer. A comparative study along with an own technique for the performance of exemplary parallel and node parallel strategies on a cluster computer were performed by Pethick et al. [4]. The performance of various sizes of neural networks, miscellaneous dataset sizes, and the number of processors was studied. The affirmative and the negative sides of their proposed method were discussed. The cost equation introduced in their method ...

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Java Fast Sockets: Enabling high-speed Java communications on high performance clusters

Cited by 19 publications

References 19 publications

F-MPJ: scalable Java message-passing communications on parallel systems

F-MPJ: scalable Java message-passing communications on parallel systems

Java for high performance computing

Parallel computing for artificial neural network training

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